Unsupervised Computer Vision: The Current State of the Art

This presentation was originally given at a styling research presentation at Stitch Fix, where I talk about some of the recent progress in the field of unsupervised deep learning methods for image analysis. It includes descriptions of Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), their hybrid (VAE/GAN), Generative Moment Matching Networks (GMMN), and Adversarial Autoencoders.

60.
TAKEAWAY
http://arxiv.org/pdf/1512.09300v1.pdf
We are trying to get away from pixels to begin with so why use pixel distance as
metric?

61.
TAKEAWAY
http://arxiv.org/pdf/1512.09300v1.pdf
Learned similarity metric provides feature-level distance rather than pixel-level.
We are trying to get away from pixels to begin with so why use pixel distance as
metric?

62.
TAKEAWAY
http://arxiv.org/pdf/1512.09300v1.pdf
Learned similarity metric provides feature-level distance rather than pixel-level.
We are trying to get away from pixels to begin with so why use pixel distance as
metric?
Latent space of a GAN with the encoder of a VAE

63.
TAKEAWAY
http://arxiv.org/pdf/1512.09300v1.pdf
Learned similarity metric provides feature-level distance rather than pixel-level.
We are trying to get away from pixels to begin with so why use pixel distance as
metric?
Latent space of a GAN with the encoder of a VAE
…BUT NOT THAT EASY TO TRAIN

69.
DESCRIPTION
Want to create an auto-encoder whose “code space” has a
distribution matching an arbitrary speciﬁed prior.
Like VAE, but instead of using Gaussian KL Div., use adversarial
procedure to match coding dist. to prior.